The methods and means for transacting commerce continue to evolve in close step with technological advances. For instance, the traditional ways of selling goods and services, the so-called “brick and mortar” approach, have expanded into remote sales through mail order and telephonic catalog sales and television-based shopping “networks.” Electronic commerce (“e-commerce”) presents the latest approach to transacting remote sales and related commerce.
E-commerce is primarily computer network-based and requires a three-part support infrastructure. First, individual consumers must have some form of client computer system, such as a personal computer typically executing a browser application. Second, businesses must field a host computer system executing a server application and an associated database. The database ordinarily stores information on the goods and services offered. Finally, the host computer system must be interconnected to each client computer system via a data network or similar form of interconnectivity. The data network can include intranetworks, also known as local area networks, and wide area networks, including public information internetworks, such as the Internet, and any combination thereof.
Most e-commerce systems are Web-based. Typically, the host computer system executes a server application for presenting a Web site that creates a virtual, user-readable “storefront.” The storefront is actually a series of downloadable Web pages structured in a hierarchical manner with embedded hyperlinks connecting to other related Web pages and content. The Web site is organized as a catalog of goods and services and includes means for secure purchasing. During operation, consumers transact commerce in a purchasing session consisting of requests for Web pages sent to and replies received from the host computer system.
E-commerce differs from traditional commerce means in several respects. Unlike traditional methods, the bulk of interaction between the consumer and vendor is through an impassive computer system and there is generally little to no opportunity to offer person-to-person, individualized sales and service. As well, the immediacy of purchasing and ease of comparison shopping results in low customer loyalty. Moreover, competitive drivers short-circuit the selling process by pro-actively soliciting sales with targeted specials sold at low margins. These competitive drivers work to entice a consumer to visit a competing vendor's Web site, potentially resulting in lost sales. E-commerce vendors attempt to address these differences by incorporating presentation and demographic business models into their Web sites.
Presentation models describe the physical layout and functionality of a virtual storefront. Presentation models are the Web-based equivalent of conventional consumer marketing. However, the effectiveness of a presentation model is difficult to judge due to the lack of subjective customer feedback. Conventional measurement methodologies for brick-and-mortar storefronts fail to provide sufficient an adequate solution. For instance, sales volumes and repeat Web site visits only partially reflect a Web site's effectiveness. Incomplete transactions and failed product searches are typically not measured nor analyzed yet could provide valuable insight into a Web site's effectiveness.
The demographic model implements the actual sales model based on statistical and behavioral models of measured and predicted consumer buying habits. Conventionally, demographic data is fairly static and is generally collected and processed periodically to determine consumer behavioral and purchasing trends. Demographic analysis is performed generally through applied artificial intelligence and statistical modeling. Persuasive factors and dependent variables are identified and weighed and, if necessary, new demographic models are built. However, e-commerce-based demographics tend to fluctuate much more rapidly than conventional demographics and periodic processing can result in lost sales volume. Depending upon the e-commerce Web site, both presentation and demographic models can age at an unknown rate.
In the prior art, click stream analysis has been used to evaluate their business models. Theoretically, every consumer's visit can be tracked, step-by-step, by collecting and storing the “click stream” of Web pages and content selections made during a given visit to the vendor's Web site. These click streams can be analyzed to determine purchasing trends and consumer behaviors. However, click stream analysis has historically not been performed due to the extremely high volume of traffic. Moreover, the off-line processing techniques used to evaluate demographic models are based on relatively static data sets. Such processing techniques are slow and ill-suited for dynamic e-commerce applications.
Therefore, there is a need for an approach to dynamically analyzing and evaluating business models incorporated into e-commerce Web sites. Preferably, such an approach would utilize click stream data representing a path through a Web site. Such an approach could be used to validate presentation and demographic models in a responsive, potentially near real-time manner.
There is a further need for an approach to collecting and analyzing large data sets of on-line streams of Web page and content selections. Such an approach could be used to form structured data sets amenable to conventional data mining techniques.